Nuclear Magnetic Resonance Based Investigations of Contaminant Interactions with Soil Organic Matter
Bibliographic record
Abstract
Contaminant interactions with soil organic matter (SOM) are central to understanding the fate and transport of chemicals in soil environments. Elucidation of sorption processes will facilitate the efficiency of passive remedial methods and improve the accuracy of risk assessment models. Early studies in the 1960s identified a relationship between SOM and the sorption of chemicals and laid the foundation for an area of research which is still active today. The onset of analytical instrumentation assisted the characterization of SOM chemical fractions, namely the fulvic acid (FA) and humic acid (HA) fractions. The employment of SOM chemical fractions in contaminant sorption studies has produced many empirical relationships between contaminant sorption behavior and SOM structure. More recently, molecular‐level techniques such as nuclear magnetic resonance (NMR) spectroscopy have been applied to examine specific interactions between contaminants and SOM fractions. These methods enable direct studies and are likely to further improve the fundamental understanding of contaminant interactions with SOM in the near future. For instance, NMR techniques should produce mechanistic information that will enable the accurate explanation of sorption phenomena at the macroscopic and landscape level. In addition to SOM chemical structure, researchers must consider the organic matter physical conformation at the soil–water interface because chemical methods provide structural information of the whole sample but do not provide detail about their physical architecture within the soil. This manuscript highlights studies which have examined contaminant interactions at the macroscopic‐ and molecular‐level and demonstrates the common themes stemming from different levels of investigation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".